Abstract: In recent years, with the development of microarray technique, discovery ofuseful knowledge from microarray data has become very important. Biclusteringis a very useful data mining technique for discovering genes which have similarbehavior. In microarray data, several objectives have to be optimizedsimultaneously and often these objectives are in conflict with each other. AMulti Objective model is capable of solving such problems. Our method proposesa Hybrid algorithm which is based on the Multi Objective Particle SwarmOptimization for discovering biclusters in gene expression data. In our method,we will consider a low level of overlapping amongst the biclusters and try tocover all elements of the gene expression matrix. Experimental results in thebench mark database show a significant improvement in both overlap amongbiclusters and coverage of elements in the gene expression matrix.